Pannexin 1 Regulates Dendritic Protrusion Dynamics in Immature Cortical Neurons
Bibliographic record
Abstract
Abstract The integration of neurons into networks relies on the formation of dendritic spines. These specialized structures arise from dynamic filopodia-like dendritic protrusions. It was recently reported that cortical neurons lacking the channel protein pannexin 1 (PANX1) exhibited higher dendritic spine densities. Here, we expanded on those findings to investigate, at an earlier developmental time point (with more abundant dendritic protrusions), whether differences in the properties of dendritic protrusion dynamics could contribute to this previously discovered phenomenon. Using a fluorescent membrane tag (mCherry-CD9-10) to visualize dendritic protrusions in developing neurons [at 10 d in vitro (DIV10)], we confirmed that lack of PANX1 led to higher protrusion density, while transient transfection of Panx1 led to decreased protrusion density. To quantify the impact of PANX1 expression on protrusion formation, elimination, and motility, we used live cell imaging in DIV10 neurons (one frame every 5 s for 10 min). We discovered that at DIV10, loss of PANX1 stabilized protrusions. Notably, re-expression of PANX1 in Panx1 knock-out (KO) neurons resulted in a significant increase in protrusion motility and turnover. In summary, these new data revealed that PANX1 could regulate the development of dendritic spines, in part, by controlling dendritic protrusion dynamics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".